# Identification of B cell subsets based on antigen receptor sequences using deep learning

## TL;DR

This paper introduces BCR-SORT, a deep learning model that predicts B cell subsets from BCR sequences, enabling better understanding of B cell responses without the need for physical isolation.

## Contribution

The novelty lies in using deep learning to predict B cell subsets from BCR sequences, capturing activation and maturation signatures.

## Key findings

- BCR-SORT improves reconstruction of BCR phylogenetic trees.
- It reveals inter-individual heterogeneity in B cell evolution toward Omicron-binding memory B cells in vaccine recipients.

## Abstract

B cell receptors (BCRs) denote antigen specificity, while corresponding cell subsets indicate B cell functionality. Since each B cell uniquely encodes this combination, physical isolation and subsequent processing of individual B cells become indispensable to identify both attributes. However, this approach accompanies high costs and inevitable information loss, hindering high-throughput investigation of B cell populations. Here, we present BCR-SORT, a deep learning model that predicts cell subsets from their corresponding BCR sequences by leveraging B cell activation and maturation signatures encoded within BCR sequences. Subsequently, BCR-SORT is demonstrated to improve reconstruction of BCR phylogenetic trees, and reproduce results consistent with those verified using physical isolation-based methods or prior knowledge. Notably, when applied to BCR sequences from COVID-19 vaccine recipients, it revealed inter-individual heterogeneity of evolutionary trajectories towards Omicron-binding memory B cells. Overall, BCR-SORT offers great potential to improve our understanding of B cell responses.